Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on cl...

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Detalles Bibliográficos
Autores principales: Burrello, Jacopo, Gallone, Guglielmo, Burrello, Alessio, Jahier Pagliari, Daniele, Ploumen, Eline H., Iannaccone, Mario, De Luca, Leonardo, Zocca, Paolo, Patti, Giuseppe, Cerrato, Enrico, Wojakowski, Wojciech, Venuti, Giuseppe, De Filippo, Ovidio, Mattesini, Alessio, Ryan, Nicola, Helft, Gérard, Muscoli, Saverio, Kan, Jing, Sheiban, Imad, Parma, Radoslaw, Trabattoni, Daniela, Giammaria, Massimo, Truffa, Alessandra, Piroli, Francesco, Imori, Yoichi, Cortese, Bernardo, Omedè, Pierluigi, Conrotto, Federico, Chen, Shao-Liang, Escaned, Javier, Buiten, Rosaly A., Von Birgelen, Clemens, Mulatero, Paolo, De Ferrari, Gaetano Maria, Monticone, Silvia, D’Ascenzo, Fabrizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224705/
https://www.ncbi.nlm.nih.gov/pubmed/35743777
http://dx.doi.org/10.3390/jpm12060990

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224705/
https://www.ncbi.nlm.nih.gov/pubmed/35743777
http://dx.doi.org/10.3390/jpm12060990

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